U.S. patent application number 13/872217 was filed with the patent office on 2014-10-30 for content delivery infrastructure with non-intentional feedback parameter provisioning.
This patent application is currently assigned to International Business Machines Corporation. The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to Marcos Dias De Assuncao, Fernando Luiz Koch, Marco Aurelio Stelmar Netto.
Application Number | 20140325068 13/872217 |
Document ID | / |
Family ID | 51790271 |
Filed Date | 2014-10-30 |
United States Patent
Application |
20140325068 |
Kind Code |
A1 |
Assuncao; Marcos Dias De ;
et al. |
October 30, 2014 |
CONTENT DELIVERY INFRASTRUCTURE WITH NON-INTENTIONAL FEEDBACK
PARAMETER PROVISIONING
Abstract
A shared resource system, method of managing shared resources
and services and a computer program product therefor. Service
provider computers (e.g., cloud computers) including a resource
management system, selectively make resource capacity available to
networked client devices. Stored resource configuration parameter
are collected from non-intentional haptic input to mobile client
devices. The resource management system provisions resources for
mobile clients based on resource configuration parameters.
Non-intentional haptic input is provided as non-intentional gesture
feedback, and evaluated to selectively update stored resource
configuration parameters.
Inventors: |
Assuncao; Marcos Dias De;
(Sao Paulo, BR) ; Koch; Fernando Luiz; (Sao Paulo,
BR) ; Netto; Marco Aurelio Stelmar; (Sao Paulo,
BR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Assignee: |
International Business Machines
Corporation
Armonk
NY
|
Family ID: |
51790271 |
Appl. No.: |
13/872217 |
Filed: |
April 29, 2013 |
Current U.S.
Class: |
709/226 |
Current CPC
Class: |
Y02D 10/36 20180101;
H04L 67/30 20130101; H04L 41/5019 20130101; G06F 9/5072 20130101;
Y02D 10/22 20180101; Y02D 10/00 20180101; H04L 41/5096 20130101;
H04L 41/147 20130101; G06F 2209/5019 20130101; H04L 43/0817
20130101; G06F 3/011 20130101; H04L 41/082 20130101 |
Class at
Publication: |
709/226 |
International
Class: |
H04L 12/911 20060101
H04L012/911 |
Claims
1. A shared resource system comprising: one or more service
provider computers including a resource management system, said one
or more service provider computers selectively making resource
capacity available; a network, client devices connecting to said
one or more service provider computers over said network; a
resource configuration parameter store storing resource
configuration parameters collected from non-intentional haptic
input to mobile client devices, said resource management system
provisioning resources for said mobile client devices responsive to
stored said resource configuration parameters, said non-intentional
haptic input being provided to said resource management system as
non-intentional gesture feedback; and means for evaluating
non-intentional gesture feedback from said mobile devices and
selectively updating stored resource configuration parameters.
2. A shared resource system as in claim 1, wherein said resource
management system comprises: a user interface for interfacing
non-intentional gesture feedback from said mobile client devices; a
resource provisioning unit provisioning services requested for said
mobile client devices responsive to said stored resource
configuration parameters, said one or more service provider
computers hosting said provisioned services; a load monitoring unit
monitoring actual load from hosted said services; a demand
prediction unit predicting resource demand for each hosted service;
and a feedback meaning adjustment unit comparing actual load
against predicted resource demand, said feedback meaning adjustment
unit adjusting gesture translations associated with non-intentional
gestures responsive to comparison results.
3. A shared resource system as in claim 1, wherein said means for
evaluating said non-intentional gesture feedback comprises: a
gesture evaluator selecting gesture sequences responsive to said
non-intentional gesture feedback; a group feedback evaluator
collectively evaluating selected gesture sequences from mobile
devices identified with a group; and a parameter extractor
extracting resource allocation parameters from feedback evaluation
results.
4. A shared resource system as in claim 3, wherein said gesture
evaluator evaluates non-intentional feedback information for
individual mobile devices to assess and select said gesture
sequences impacting resource consumption and system
utilization.
5. A shared resource system as in claim 3, wherein said group
feedback evaluator collects individual feedback evaluation results
for groups and evaluates the collections to assess how different
group behaviors influence system load.
6. A shared resource system as in claim 1, further comprising a
plurality of client devices connected to said network and networked
with said one or more service provider computers, one or more of
said plurality of client devices being a mobile device including a
haptic capable user interface receiving non-intentional haptic
input to said mobile device.
7. A shared resource system as in claim 6, wherein said one or more
service provider computers are cloud computers operating in a cloud
environment, said plurality of client devices are cloud client
devices, and said non-intentional haptic input quantifies
interaction with active applications.
8. A shared resource system as in claim 7, wherein said
non-intentional haptic input further comprises physical interaction
with said mobile device including gestures, tactile manipulation,
surface contact, zooming actions and tapping.
9. A method of managing allocated resources, said method
comprising: hosting services provisioned on one or more provider
computers for a mobile device; receiving feedback responsive to
non-intentional input to said mobile device; determining whether
received non-intentional said feedback indicates whether actual
system load matches expected load considered in provisioning for
said hosted services; and adjusting load prediction for any service
where actual load exceeds expected load, each hosted service being
provisioned subsequently responsive to the adjusted load
prediction.
10. A method of managing allocated resources as in claim 9, wherein
determining whether non-intentional feedback matches actual system
load is done for each hosted service and comprises: evaluating
non-intentional feedback data for each user of said hosted
services; evaluating non-intentional feedback data for user groups
using said hosted services; and generating load prediction
parameters for said hosted services, said generated load prediction
parameters being used to adjust load prediction parameters for
provisioning said hosted services.
11. A method of managing allocated resources as in claim 10,
further comprising: monitoring said hosted services for actual
system load; and predicting load for said hosted services
responsive to said load prediction parameters; adjusting feedback
translated meaning responsive to said actual system load and said
load prediction.
12. A method of managing allocated resources as in claim 9, wherein
determining whether non-intentional feedback matches actual system
load is done for each hosted service and adjusting load prediction
comprises: selecting one of said hosted services; comparing the
current resource allocation against a predicted load over a prior
selected time period for the selected said hosted service;
adjusting load prediction for said selected hosted service
responsive to any comparison difference over a selected number of
selected time periods; selectively adjusting load prediction for
said selected hosted service responsive to non-intentional said
feedback over said selected number of selected time periods; and
selecting another of said hosted services and returning to compare
the current resource allocation against predicted load over for the
selected said hosted service until all said hosted service are
selected.
13. A method of managing allocated resources as in claim 12,
wherein determining whether non-intentional feedback matches
further comprises: determining whether predicted load exceeds
current resource allocation; identifying allocation parameters
changes responsive to the amount predicted load exceeds current
resource allocation; and providing an indication of allocation
parameter changes to meet load prediction.
14. A method of managing allocated resources as in claim 9, wherein
said allocated resources are cloud resources, one or more provider
computers are cloud computers operating in a cloud environment,
said mobile device is a cloud client device, and said haptic
feedback comprises non-intentional input to said mobile device.
15. A computer program product for managing allocated cloud
resources, said computer program product comprising a computer
usable medium having computer readable program code stored thereon,
said computer readable program code causing a computer executing
said code to: host services provisioned on one or more cloud
computers for a mobile device; receive feedback responsive to
non-intentional input to said mobile device; determine whether
received non-intentional said feedback indicates whether actual
system load matches expected load considered in provisioning said
hosted services; and adjust load prediction for any service where
actual load exceeds expected load, each hosted service being
provisioned subsequently responsive to the adjusted load
prediction.
16. A computer program product for managing allocated cloud
resources as in claim 15, wherein determining whether
non-intentional feedback matches actual system load is done for
each hosted service and causes said computer executing said code
to: evaluate non-intentional feedback data for each user of said
hosted services; evaluate non-intentional feedback data for user
groups using said hosted services; and generate load prediction
parameters for said hosted services, and use said generated load
prediction parameters to adjust load prediction parameters for
provisioning said hosted services.
17. A computer program product for managing allocated cloud
resources as in claim 16, further causing said computer executing
said code to: Monitor said hosted services for actual system load;
and predict load for said hosted services responsive to said load
prediction parameters; adjust feedback translated meaning
responsive to said actual system load and said load prediction.
18. A computer program product for managing allocated cloud
resources as in claim 15, wherein determining whether
non-intentional feedback matches actual system load is done for
each hosted service and adjusting load prediction causes said
computer executing said code to: select one of said hosted
services; compare the current resource allocation against a
predicted load over a prior selected time period for the selected
said hosted service; adjust load prediction for said selected
hosted service responsive to any comparison difference over a
selected number of selected time periods; selectively adjust load
prediction for said selected hosted service responsive to
non-intentional said feedback over said selected number of selected
time periods; and select another of said hosted services and return
to compare the current resource allocation against predicted load
over for the selected said hosted service until all said hosted
service are selected.
19. A computer program product for managing allocated cloud
resources as in claim 18, wherein determining whether
non-intentional feedback matches further causes said computer
executing said code to: determine whether predicted load exceeds
current resource allocation; identify allocation parameters changes
responsive to the amount predicted load exceeds current resource
allocation; and provide an indication of allocation parameter
changes to meet load prediction.
20. A computer program product for managing allocated cloud
resources, said computer program product comprising a computer
usable medium having computer readable program code stored thereon,
said computer readable program code comprising: computer readable
program code means for managing one or more cloud provider
computers selectively making resource capacity available; computer
readable program code means for receiving non-intentional gesture
feedback from haptic input to mobile client devices; computer
readable program code means for storing resource configuration
parameters collected from gesture feedback; computer readable
program code means for provisioning resources for mobile client
devices responsive to stored said resource configuration
parameters; and computer readable program code means for evaluating
non-intentional gesture feedback from said mobile devices and
selectively updating stored resource configuration parameters.
21. A computer program product for managing allocated cloud
resources as in claim 20, said computer readable program code means
for managing comprising: computer readable program code means for
interfacing non-intentional gesture feedback from said mobile
client devices; computer readable program code means for
provisioning services requested for said mobile client devices
responsive to said stored gesture feedback parameters; computer
readable program code means for hosting said provisioned services;
computer readable program code means for monitoring actual load
from hosted said services; computer readable program code means for
predicting resource demand for each hosted service; and computer
readable program code means for comparing actual load against
predicted resource demand, and adjusting gesture translations
associated with non-intentional gestures responsive to comparison
results.
22. A computer program product for managing allocated cloud
resources as in claim 20, said computer readable program code means
for evaluating said non-intentional gesture feedback comprises:
computer readable program code means for selecting gesture
sequences responsive to said non-intentional gesture feedback;
computer readable program code means for collectively evaluating
selected gesture sequences from mobile devices identified with a
group; and computer readable program code means for extracting
resource allocation parameters from feedback evaluation
results.
23. A computer program product for managing allocated cloud
resources as in claim 22, wherein said computer readable program
code means for evaluating evaluates non-intentional feedback
information for individual mobile devices to assess and select said
gesture sequences impacting resource consumption and system
utilization.
24. A computer program product for managing allocated cloud
resources as in claim 22, wherein said computer readable program
code means for collectively evaluating collects individual feedback
evaluation results for groups and evaluates the collections to
assess how different group behaviors influence system load.
25. A computer program product for managing allocated cloud
resources as in claim 20, further comprising computer readable
program code means for a haptic capable user interfacing a mobile
device with haptic sensors sensing non-intentional haptic input;
said non-intentional haptic input quantifies interaction with
active applications and comprising physical interaction with said
mobile device including gestures, tactile manipulation, surface
contact, zooming actions and tapping.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Field of the Invention
[0002] The present invention is related to allocating shared
resources and more particularly to automatically adjusting resource
allocation and automatic demand prediction in real time for highly
interactive applications based on non-intentional haptic
feedback.
[0003] 2. Background Description
[0004] Typically, provisioning and managing shared information
technology (IT) and especially cloud infrastructure resources
involves scheduling jobs according to deadlines, allocating
resources to scheduled jobs, setting job priorities and predicting
load and performance to maximize utilization. Job scheduling
management is described, for example, by Feitelson, "Parallel job
scheduling--a status report," Proceedings of JSSPP, 2005; and by
Takefusa et al. "A Study of Deadline Scheduling for Client-Server
Systems on the Computational Grid," Proceedings of HPDC, 2001.
[0005] In allocating resources and, further, in determining
expected consolidation opportunities, factors considered may
include, for example, resource utilization, application response
time and energy consumption. Load estimates indicate the typical
predicted user load, which varies with actual use over time,
depending on how each user interacts with a respective application.
Several well known load prediction techniques are available, some
of which consider user device interactions independent of whether
allocation may be improved. Typically, however, service providers
have monitored device requests on the provider (server) side to
measure the degree of user interaction with cloud based
applications. Utilization estimates project how many users are
expected to utilize a particular service over time.
[0006] While these techniques may work well with stationary or for
pseudo-stationary clients, client mobility can render these
techniques ineffective. Mobile client devices typically run local
applications that manage digital content consumption. State of the
art mobile devices, such as stand alone, handheld multimedia
players, tablet computers, personal digital assistants (PDAs) and
smart phones have increasingly become major consumers of remotely
stored and/or streaming cloud content.
[0007] Thus, there is a need for improved resource allocation
strategies that consider feedback from mobile users; and more
particularly, there is a need for capturing and using mobile user
feedback for more accurately determining service usage tendencies
and for more accurately determining when peak demand is likely to
occur.
SUMMARY OF THE INVENTION
[0008] A feature of the invention is resource allocation and
adjustment based on mobile user responses;
[0009] Another feature of the invention is resource allocation
adjustment on the fly for highly interactive applications based on
real time user responses;
[0010] Yet another feature of the invention is automatic resource
allocation adjustment and demand prediction for highly interactive
applications based on real time non-intentional haptic feedback,
changing estimated/expected mobile user average demand on the
fly.
[0011] The present invention relates to a shared resource system,
method of managing shared resources and services and a computer
program product therefor. Service provider computers (e.g., cloud
computers) including a resource management system, selectively make
resource capacity available to networked client devices. Stored
resource configuration parameter are collected from non-intentional
haptic input to mobile client devices. The resource management
system provisions resources for mobile clients based on resource
configuration parameters. Non-intentional haptic input is provided
as non-intentional gesture feedback, and evaluated to selectively
update stored resource configuration parameters.
BRIEF DESCRIPTION OF THE DRAWINGS
[0012] The foregoing and other objects, aspects and advantages will
be better understood from the following detailed description of a
preferred embodiment of the invention with reference to the
drawings, in which:
[0013] FIG. 1 depicts a cloud computing node according to an
embodiment of the present invention;
[0014] FIG. 2 depicts a cloud computing environment according to an
embodiment of the present invention;
[0015] FIG. 3 depicts abstraction model layers according to an
embodiment of the present invention;
[0016] FIGS. 4A-B show an example of a haptic enabled mobile or
hand held device cloud computing node and interface according to a
preferred embodiment of the present invention;
[0017] FIG. 5 shows an example of data flow through cloud managed
resources provisioned for a preferred hand held device, capable of
receiving and interpreting haptic and tactile interactions
operating through a preferred haptic interface;
[0018] FIGS. 6A-F show examples of tables used in feedback
interpretation for the data flow;
[0019] FIG. 7 shows an example of pseudo-code for iteratively
adjusting demand prediction for mobile devices based on
non-intentional haptic feedback.
DESCRIPTION OF PREFERRED EMBODIMENTS
[0020] It is understood in advance that although this disclosure
includes a detailed description on cloud computing, implementation
of the teachings recited herein are not limited to a cloud
computing environment. Rather, embodiments of the present invention
are capable of being implemented in conjunction with any other type
of computing environment now known or later developed and as
further indicated hereinbelow.
[0021] Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
[0022] Characteristics are as follows:
[0023] On-demand self-service: a cloud consumer can unilaterally
provision computing capabilities, such as server time and network
storage, as needed automatically without requiring human
interaction with the service's provider.
[0024] Broad network access: capabilities are available over a
network and accessed through standard mechanisms that promote use
by heterogeneous thin or thick client platforms (e.g., mobile
phones, laptops, and PDAs).
[0025] Resource pooling: the provider's computing resources are
pooled to serve multiple consumers using a multi-tenant model, with
different physical and virtual resources dynamically assigned and
reassigned according to demand. There is a sense of location
independence in that the consumer generally has no control or
knowledge over the exact location of the provided resources but may
be able to specify location at a higher level of abstraction (e.g.,
country, state, or datacenter).
[0026] Rapid elasticity: capabilities can be rapidly and
elastically provisioned, in some cases automatically, to quickly
scale out and rapidly released to quickly scale in. To the
consumer, the capabilities available for provisioning often appear
to be unlimited and can be purchased in any quantity at any
time.
[0027] Measured service: cloud systems automatically control and
optimize resource use by leveraging a metering capability at some
level of abstraction appropriate to the type of service (e.g.,
storage, processing, bandwidth, and active user accounts). Resource
usage can be monitored, controlled, and reported providing
transparency for both the provider and consumer of the utilized
service. Moreover, the present invention provides for client
self-monitoring for adjusting individual resource allocation and
configuration on-the-fly for optimized resource allocation in real
time and with operating costs and energy use minimized.
[0028] Service Models are as follows:
[0029] Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
[0030] Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
[0031] Infrastructure as a Service (IaaS): the capability provided
to the consumer is to provision processing, storage, networks, and
other fundamental computing resources, sometimes referred to as a
hypervisor, where the consumer is able to deploy and run arbitrary
software, which can include operating systems and applications. The
consumer does not manage or control the underlying cloud
infrastructure but has control over operating systems, storage,
deployed applications, and possibly limited control of select
networking components (e.g., host firewalls).
[0032] Deployment Models are as follows:
[0033] Private cloud: the cloud infrastructure is operated solely
for an organization. It may be managed by the organization or a
third party and may exist on-premises or off-premises.
[0034] Community cloud: the cloud infrastructure is shared by
several organizations and supports a specific community that has
shared concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
[0035] Public cloud: the cloud infrastructure is made available to
the general public or a large industry group and is owned by an
organization selling cloud services.
[0036] Hybrid cloud: the cloud infrastructure is a composition of
two or more clouds (private, community, or public) that remain
unique entities but are bound together by standardized or
proprietary technology that enables data and application
portability (e.g., cloud bursting for load-balancing between
clouds).
[0037] A cloud computing environment is service oriented with a
focus on statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure comprising a network of interconnected nodes.
[0038] Referring now to FIG. 1, a schematic of an example of a
cloud computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
[0039] In cloud computing node 10 there is a computer system/server
12, which is operational with numerous other general purpose or
special purpose computing system environments or configurations.
Examples of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
[0040] Computer system/server 12 may be described in the general
context of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
[0041] As shown in FIG. 1, computer system/server 12 in cloud
computing node 10 is shown in the form of a general-purpose
computing device. The components of computer system/server 12 may
include, but are not limited to, one or more processors or
processing units 16, a system memory 28, and a bus 18 that couples
various system components including system memory 28 to processor
16.
[0042] Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component
Interconnects (PCI) bus.
[0043] Computer system/server 12 typically includes a variety of
computer system readable media. Such media may be any available
media that is accessible by computer system/server 12, and it
includes both volatile and non-volatile media, removable and
non-removable media.
[0044] System memory 28 can include computer system readable media
in the form of volatile memory, such as random access memory (RAM)
30 and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
memory 28 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
[0045] Program/utility 40, having a set (at least one) of program
modules 42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
[0046] Computer system/server 12 may also communicate with one or
more external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples, include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
[0047] Referring now to FIG. 2, illustrative cloud computing
environment 50 is depicted. As shown, cloud computing environment
50 comprises one or more cloud computing nodes 10 with which local
computing devices used by cloud consumers, such as, for example,
personal digital assistant (PDA) or cellular telephone 54A, desktop
computer 54B, laptop computer 54C, and/or automobile computer
system 54N may communicate. Nodes 10 may communicate with one
another. They may be grouped (not shown) physically or virtually,
in one or more networks, such as Private, Community, Public, or
Hybrid clouds as described hereinabove, or a combination thereof.
This allows cloud computing environment 50 to offer infrastructure,
platforms and/or software as services for which a cloud consumer
does not need to maintain resources on a local computing device. It
is understood that the types of computing devices 54A-N shown in
FIG. 2 are intended to be illustrative only and that computing
nodes 10 and cloud computing environment 50 can communicate with
any type of computerized device over any type of network and/or
network addressable connection (e.g., using a web browser).
[0048] Referring now to FIG. 3, a set of functional abstraction
layers provided by cloud computing environment 50 (FIG. 2) is
shown. It should be understood in advance that the components,
layers, and functions shown in FIG. 3 are intended to be
illustrative only and embodiments of the invention are not limited
thereto. As depicted, the following layers and corresponding
functions are provided:
[0049] Hardware and software layer 60 includes hardware and
software components. Examples of hardware components include
mainframes, in one example IBM.RTM. zSeries.RTM. systems; RISC
(Reduced Instruction Set Computer) architecture based servers, in
one example IBM pSeries.RTM. systems; IBM xSeries.RTM. systems; IBM
BladeCenter.RTM. systems; storage devices; networks and networking
components 62. Examples of software components include network
application server software, in one example IBM WebSphere.RTM.
application server software; and database software, in one example
IBM DB2.RTM. database software. (IBM, zSeries, pSeries, xSeries,
BladeCenter, WebSphere, and DB2 are trademarks of International
Business Machines Corporation registered in many jurisdictions
worldwide).
[0050] Virtualization layer 64 provides an abstraction layer from
which the following examples of virtual entities may be provided:
virtual servers; virtual storage; virtual networks, including
virtual private networks; virtual applications and operating
systems; and virtual clients.
[0051] In one example, management layer 66 may provide the
functions described below. Resource provisioning 68 provides
dynamic procurement of computing resources and other resources that
are utilized to perform tasks within the cloud computing
environment. Metering and Pricing 70 provide cost tracking as
resources are utilized within the cloud computing environment, and
billing or invoicing for consumption of these resources. In one
example, these resources may comprise application software
licenses. Security provides identity verification for cloud
consumers and tasks, as well as protection for data and other
resources. User portal 72 provides access to the cloud computing
environment for consumers and system administrators. Service level
management 74 provides cloud computing resource allocation and
management such that required service levels are met. Service Level
Agreement (SLA) planning and fulfillment 76 provide pre-arrangement
for, and procurement of, cloud computing resources for which a
future requirement is anticipated in accordance with an SLA.
[0052] Workloads layer 78 provides examples of functionality for
which the cloud computing environment may be utilized. Examples of
workloads and functions which may be provided from this layer
include: mapping and navigation; software development and lifecycle
management; virtual classroom education delivery 80; data analytics
processing; transaction processing; and Mobile Desktop 82.
[0053] FIGS. 4A-B show an example of a mobile or hand held device
100 operating as a cloud computing node 10 with a haptic enabled
interface 110 according to a preferred embodiment of the present
invention. Current and next generation state of the art mobile
devices manufacturers are equipping new devices 100 with haptic
technology, e.g., 110. A preferred mobile device 100 is capable of
receiving and interpreting haptic and tactile interactions in
cooperation with haptic enabled cloud infrastructure of FIGS.
1-3.
[0054] In this example, the preferred hand held device 100 (e.g., a
tablet computer or a smart phone such as cellular telephone 54A)
includes touch screen 102 as an input and output peripheral (I/O).
Embedded haptic technology includes one or more tactile sensors 104
that measure forces exerted on the device user interface (UI) 112,
e.g., on touch screen 102 and between touch screen 102 and housing
106. The user interface 112 is resident in internal device
electronics, e.g., in housing 106. The device 100 consumes services
hosted on cloud computers 10 and communicates with management
services from the management layer 66 through Application
Programming Interfaces (APIs, e.g., in user portal 72) over a
network (e.g., 62), wirelessly 108 in this example.
[0055] In addition to sensing intentional user input, haptic
sensors 104 sense non-intentional input, referred to herein as
"non-intentional user feedback." The haptic interface 110 receives
and monitors incidental haptic input from the preferred hand held
device 100 in combination and coordination with haptic enabled
cloud infrastructure. Non-intentional user feedback data passes
back through management services accessible via APIs, where one or
more shared systems 10 determine the degree of user interaction,
and/or infer user excitement experienced with a corresponding
application or service.
[0056] Preferably, two primary components or modules cooperate with
the haptic enabled interface 110 in sensing and interpreting
non-intentional user feedback, a haptic app 114 and a transparent,
cloud haptic application 116. The preferred haptic app 114 is
resident on the device 100. The haptic app 114 may be installed in
Mobile Desktop 82 in cloud computing environment 50.
Correspondingly, the cloud haptic application 116 is active in the
cloud, e.g., on one of the cloud computers 10. The cloud haptic
application 116 provides cloud services and receives and interprets
non-intentional input. The cloud haptic application 116 further
receives notifications indicating how the device 100 user interacts
with the resident haptic app 114.
[0057] Typically, mobile applications or apps are considered as
belonging to one of two categories, highly interactive apps and
limited interaction apps. A highly interactive app is an app that
requires substantial user interaction, e.g., by frequently touching
a mobile device screen 102. This touching may include selecting
objects, dragging objects, and moving the device, e.g., rotating
the device 100. For example, a game or other app may require
interaction using a touch screen 102, accelerometers, haptic
sensors 104 and tactile sensors. A limited interaction app requires
very little sensor input, e.g., web browsing. The present invention
automatically adjusts resource demand prediction in real time,
changing estimated/expected per user average demand on the fly,
based on historic increases in interaction over a past time
horizon. App interaction type defines the degree of adjustment
(increase or reduction) of resource demand for each user. Overall
service resource prediction is iterative with a new iteration after
each average user demand update.
[0058] So, the haptic interface 110 passes/feeds back data from
haptic sensors 104 for tactilely sensing and measuring user force
on the touch screen 102 through the haptic app 114 to the cloud
haptic application 116. The cloud haptic application 116 monitors
haptic activity feedback data from sensors 104, collecting and
reporting non-intentional haptic feedback to cloud managed
resources 50, where the feedback data may be stored, e.g., in
storage 34. The cloud haptic application 116 may also interpret
sense signals from sensors 104 as response feedback for additional
predictive resource allocation information.
[0059] Thus, the preferred haptic interface 110 and provisioned
cloud services 50 sense and interpret user originated physical
interaction including, for example, gestures, tactile manipulation,
surface contact, zooming actions and tapping. The provisioned cloud
services 50 may classify haptic activities as utilization events
and individually analyze feedback, as a sequence of events, and/or
collectively as events from a group of users. For example, the
group may be a community of students sharing IT infrastructure,
e.g., in a virtual classroom education delivery 80, delivering
digital educational content and educational applications.
[0060] In particular, the preferred haptic interface 110, haptic
app 114 and haptic application 116 may monitor a group of users
interacting with digital content material on touch screens 102,
collecting group feedback. Management layer 66 may service haptic
enabled mobile devices 100, through special purpose modules or,
through typical, normally provided modules that are modified for
haptic feedback analysis. The analysis results provide demand
prediction for determining resource provisioning parameters and
monitoring utilization loads.
[0061] Metering and Pricing 70 in FIG. 3, for example, may analyze
how a user group interacts with running applications for a given
resource allocation parameter set, e.g., in storage 34, based on
collected non-intentional feedback. So, for example, Metering and
Pricing 70 may estimate the probability of a peak load in near- to
middle-range term; receive and store information about user
feedback from mobile devices 100; and analyze the information to
create and update user and group profiles and to identify
application use trends.
[0062] Likewise, SLA planning and fulfillment 76 may include, for
example, predicting demand based on current resource demand and
feedback information determined by the feedback analyzer, e.g.,
Metering and Pricing 70. Analyzing feedback predicts resource
demands over a given time horizon from which SLA planning and
fulfillment 76 can recommend (e.g., to system administrators)
parameter adjustments for resource provisioning to provide reports,
and for identifying parameters to adjust to cope with the service
demands in delivering content to mobile devices. In addition,
preferably, provisioning-parameter information are tuned
automatically for the number and characteristics of resources
allocated to a given service. Service level management 74 monitors
the current resource utilization to determine past load prediction
accuracy; and corrects eventual mapping issues in mapping user
gestures to load-change characteristics.
[0063] A preferred mobile device 100 may include a module
responsible for collecting information about how a user interacts
with active applications. The data from feedback sensor(s) 104
quantify the level of force to the screen 102 and/or zoom-in,
zoom-out gestures. The haptic app 114 provides collected haptic
data to the cloud 50 to facilitate efficiently hosting services and
applications, and delivering digital content and services to the
preferred mobile device(s) 100.
[0064] FIG. 5 shows an example of haptic application 116 data flow
through cloud managed resources of FIGS. 1-3 provisioned for a
preferred hand held device 100 of FIGS. 4A-4B, with identical
features labeled identically. A resource management system 66
(e.g., on a server 10) provisions 68 content services 120 (user
applications) in virtualization layer 64 for preferred mobile
device(s) 100. Cloud services interface 120 receives
user-interaction information, e.g., through user portal or APIs 72.
The preferred mobile devices 100 monitor for, and report,
non-intentional user feedback for evaluation 124, e.g., gestures
collected by sensors 104.
[0065] A gesture evaluator 124 evaluates feedback information for
each mobile device to assess and select gesture sequences impacting
resource consumption and system utilization. A group feedback
evaluator 126 collects individual feedback evaluation results for
groups and evaluates the collections to assess how different group
behaviors influence system load, near or middle term. Then, a
parameter extractor 128 extracts resource allocation parameters
from feedback evaluation results. The resulting gesture feedback
parameters 130 are returned to the resource management system 66
and stored for subsequent resource provisioning 68.
[0066] A load monitoring unit 132 continually monitors actual load
on provisioned resources. A demand predictor 134 predicts resource
demand for each hosted service 120. A gesture translation adjuster
136 compares actual load (from monitoring 132) in real time against
the predicted 134 demand. If the real time demand matches
predictions, haptic data are correctly translated using gesture
interpretation table 138. Otherwise, one or more translation
meanings need adjustment.
[0067] FIGS. 6A-F show examples of tables used in feedback
interpretation for the haptic application 116 data flow, including
gesture interpretation table 138. The gesture interpretation table
138 maps gestures to potential influence on load each gesture may
have for a service. Gesture evaluator 124 evaluates user feedback
markers based on a gesture table 140 with recognized gestures with
respective user identifications, and on a user profile table 142
with profile information for each user based on the current gesture
interpretation table 138. Gesture evaluator 124 generates a gesture
evaluation table 144, that indicates what user gestures mean to the
load of services being used and whether the gestures indicate a
peak demand. Gesture evaluator 124 also selectively updates the
user profile table 142.
[0068] Group feedback evaluator 126 combines group profiles from a
group profile table 146 and user profiles 142 to produce a
collective load table 148 based on feedback information in gesture
evaluation table 144. Thus, the group profile table 146 includes
profile information for each group. The collective load table 148
indicates how different group behaviors might influence system load
in the near or middle-range term.
[0069] Resource provisioning 68 acts on previously made service
resource allocations, adjusting according to expected demand
increases or decreases. As noted hereinabove, load monitor 132
continually monitors actual load on provisioned resources. Further,
monitored results are compared 136 against predicted resource
demand 134 of each hosted service 120 and compared. The comparison
establishes whether to adjust gesture interpretation table 138
meanings, i.e., whether to correct/adjust expected impact in
identified demand need surges.
[0070] FIG. 7 shows an example of pseudo-code for iteratively
adjusting demand prediction 150 for mobile devices (e.g., 100 in
FIGS. 4A-B) based on non-intentional haptic feedback. In each
iteration, one hosted service (s) 152 is selected and for that
service, an allocation variable (curr_resource_allocation) is set
154 to the current allocation. Based on that current allocation,
resource demand for the next time horizon (h) is predicted 156
(predicted_demand). The prediction error (last_error) 158 for
previous time horizon is determined as the difference between the
current allocation and predicted demand
(curr_resource_allocation-predicted_demand). The predicted resource
demand is adjusted 160 over x time horizons. Thus, the adjustment
may be based solely on the most recent error (x=1), or for a less
volatile adjustment, over a selected number n of time horizons
(x=n). If interaction patterns have changed 162 for a number of
service users over the past x time horizons, the predicted resource
demand is further adjusted 164 in consideration of user interaction
with those service users. If the predicted demand exceeds the
current allocation 166, the predicted demand causes an initial
over-allocation, and a report is created 168 identifying allocation
parameters that need to be changed. Finally, the report is made
available 170, e.g., emailed, texted, displayed or printed to a
system administrator.
[0071] Thus, the present invention uses non-intentional user
feedback to refine resource demand prediction. Estimating
Information Technology (IT) resources demand may be done, for
example, using time series analysis and Kalman filters. Series
analysis is described, for example, by Box et al., Time Series
Analysis: Forecasting and Control, Prentice-Hall International,
Inc., 3rd edition, 1994. Kalman filters are described, for example,
by Kalman. "A New Approach to Linear Filtering and Prediction
Problems," Transactions of the ASME--Journal of Basic Engineering,
Vol. 82, Series D, pp. 35-45, 1960.
[0072] Accordingly, application of the present invention improves
resource allocation efficiency based on consideration of
non-intentional feedback in load prediction. Further, the present
invention pro-actively recommends resource allocation adjustments
on the fly for highly interactive applications in response to the
non-intentional feedback. Moreover, the present invention
automatically allocates and adjusts resources and demand prediction
for highly interactive applications in real time based on
non-intentional haptic feedback, changing estimated/expected per
user average demand on the fly.
[0073] While the invention has been described in terms of preferred
embodiments, those skilled in the art will recognize that the
invention can be practiced with modification within the spirit and
scope of the appended claims. It is intended that all such
variations and modifications fall within the scope of the appended
claims. Examples and drawings are, accordingly, to be regarded as
illustrative rather than restrictive.
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